U.S. patent application number 16/355231 was filed with the patent office on 2019-07-11 for semi-automated heart valve morphometry and computational stress analysis from 3d images.
The applicant listed for this patent is The Trustees of the University of Pennsylvania. Invention is credited to Brian B. Avants, Joseph H. Gorman, III, Robert C. Gorman, Benjamin M. Jackson, Alison M. Pouch, Chandra M. Sehgal, Hongzhi Wang, Paul A. Yushkevich.
Application Number | 20190213737 16/355231 |
Document ID | / |
Family ID | 51580817 |
Filed Date | 2019-07-11 |
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United States Patent
Application |
20190213737 |
Kind Code |
A1 |
Jackson; Benjamin M. ; et
al. |
July 11, 2019 |
Semi-Automated Heart Valve Morphometry And Computational Stress
Analysis From 3D Images
Abstract
A method is provided for measuring or estimating stress
distributions on heart valve leaflets by obtaining
three-dimensional images of the heart valve leaflets, segmenting
the heart valve leaflets in the three-dimensional images by
capturing locally varying thicknesses of the heart valve leaflets
in three-dimensional image data to generate an image-derived
patient-specific model of the heart valve leaflets, and applying
the image-derived patient-specific model of the heart valve
leaflets to a finite element analysis (FEA) algorithm to estimate
stresses on the heart valve leaflets. The images of the heart valve
leaflets may be obtained using real-time 3D transesophageal
echocardiography (rt-3DTEE). Volumetric images of the mitral valve
at mid-systole may be analyzed by user-initialized segmentation and
3D deformable modeling with continuous medial representation to
obtain, a compact representation of shape. The regional leaflet
stress distributions may be predicted in normal and diseased
(regurgitant) mitral valves using the techniques of the
invention.
Inventors: |
Jackson; Benjamin M.;
(Wynnewood, PA) ; Gorman; Robert C.; (Lower
Gwynedd, PA) ; Gorman, III; Joseph H.; (Lower
Gwynedd, PA) ; Pouch; Alison M.; (Philadelphia,
PA) ; Sehgal; Chandra M.; (Wayne, PA) ;
Yushkevich; Paul A.; (Wynnewood, PA) ; Avants; Brian
B.; (Philadelphia, PA) ; Wang; Hongzhi;
(Boiling Springs, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Trustees of the University of Pennsylvania |
Philadelphia |
PA |
US |
|
|
Family ID: |
51580817 |
Appl. No.: |
16/355231 |
Filed: |
March 15, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15697169 |
Sep 6, 2017 |
10235754 |
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16355231 |
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14774325 |
Sep 10, 2015 |
9779496 |
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PCT/US2014/024082 |
Mar 12, 2014 |
|
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15697169 |
|
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61788691 |
Mar 15, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 8/0858 20130101;
G06T 7/13 20170101; G06F 30/23 20200101; G06T 2200/04 20130101;
G06T 2207/20112 20130101; G06K 9/52 20130101; G06T 7/60 20130101;
A61B 8/485 20130101; G06T 2207/30048 20130101; G06K 9/6215
20130101; G06K 9/4604 20130101; G06T 7/0012 20130101; A61B 8/085
20130101; A61B 8/12 20130101; A61B 8/483 20130101; G06K 2009/4666
20130101; A61B 8/0883 20130101; G06T 3/40 20130101; G06T 2207/10136
20130101 |
International
Class: |
G06T 7/00 20060101
G06T007/00; G06T 7/13 20060101 G06T007/13; G06T 7/60 20060101
G06T007/60; G06F 17/50 20060101 G06F017/50; G06T 3/40 20060101
G06T003/40; G06K 9/62 20060101 G06K009/62; G06K 9/52 20060101
G06K009/52; G06K 9/46 20060101 G06K009/46; A61B 8/08 20060101
A61B008/08; A61B 8/12 20060101 A61B008/12 |
Claims
1. A method of measuring, estimating, or predicting biomechanical
and material properties and displacement of heart valve leaflets in
a subject, comprising: obtaining three-dimensional images of the
heart valve leaflets; segmenting the heart valve leaflets in the
three-dimensional images, in order to obtain three-dimensional a
patient-specific model of the heart valve leaflets from which
localized measurements of leaflet thickness can be computed; and
applying the image-derived patient-specific model of the heart
valve leaflets to a biomechanical analysis to estimate or predict
biomechanical and material properties and displacement of the heart
valve leaflets.
2. The method of claim 1, wherein the obtaining step comprises
imaging the heart valve leaflets using echocardiography to obtain
three-dimensional ultrasound images.
3. The method of claim 1, wherein segmenting the heart valve
leaflets is performed manually with active contour evolution, with
multi-atlas segmentation, or with a deformable modeling method.
4. The method of claim 3, wherein the heart valve leaflets are
segmented in the three-dimensional image data by user-initialized
three-dimensional active contour evolution based on region
competition.
5. The method of claim 4, wherein segmenting the heart valve
leaflets further comprises user-initialized region of interest
(ROI) extraction including construction of a two-dimensional
maximum intensity projection image along an axial dimension of an
image volume of the heart valve leaflet images and application of
adaptive histogram equalization to the projection image to enhance
an annular rim and leaflet coaptation zone of the heart valve.
6. The method of claim 5, wherein segmenting the heart valve
leaflets further comprises a user outlining the heart valve and
marking a leaflet coaptation curve in the enhanced projection
image, selecting a threshold for region competition, and using
resulting information to initialize said three-dimensional active
contour evolution.
7. The method of claim 6, wherein segmenting the heart valve
leaflets further comprises using a level set method to solve for a
final three-dimensional segmentation.
8. The method of claim 1, further comprising obtaining
three-dimensional binary images of anterior and posterior leaflets
of the valve, from which localized measurements of leaflet
thickness can be computed.
9. The method of claim 8, further comprising obtaining localized
measurements of leaflet thickness by modeling a shape of each heart
valve leaflet with three-dimensional continuous medial
representation (cm-rep).
10. The method of claim 9, further comprising obtaining a
patient-specific cm-rep of the heart valve leaflets by fitting a
deformable medial model to binary segmentations of the heart valve
leaflets by Bayesian optimization.
11. The method of claim 10, wherein each heart valve leaflet is
treated as a separate shape whose morphological skeleton comprises
a single medial manifold.
12. The method of claim 11, wherein the medial manifold and surface
boundaries of the cm-rep of each heart valve leaflet is discretely
represented by a triangulated mesh and each node of the mesh
quantifies a localized leaflet thickness measurement defined as a
chord length or distance between two boundary points associated
with that node on the mesh.
13. The method of claim 12, wherein during the Bayesian
optimization the mesh of each heart valve leaflet is deformed such
that a similarity between the mesh and its corresponding
segmentation is maximized.
14. The method of claim 13, wherein a Laplace eigenfunction basis
is defined on the mesh during fitting of the deformable medial
model such that the mesh is deformed smoothly by modifying
coefficients of a small number of basis functions rather than all
vertices of the mesh.
15. The method of claim 14, wherein the fitting of the deformable
medial model is performed in stages with increasing resolution.
16. The method of claim 10, wherein an atrial surface of the fitted
cm-rep of each heart valve leaflet is applied to the FEA algorithm
for finite element analysis using locally defined leaflet thickness
at each point on the atrial surface as input.
17. The method of claim 1, wherein the segmenting step includes
quantifying locally varying thicknesses of the heart valve leaflets
in the three-dimensional image data.
18. The method of claim 17, wherein quantifying locally varying
thicknesses of the heart valve leaflets in the three-dimensional
image data comprises applying a deformable medial model that
derives locally varying heart valve leaflet thickness measurements
from a medial axis representation of the heart valve leaflets.
19. The method of claim 18, wherein the patient-specific model of
the heart valve leaflets is applied to said FEA algorithm using
image-derived thickness measurements of the heart valve leaflets as
input material parameters.
20. The method of claim 1, wherein the heart valve is the mitral
valve.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. Ser. No.
15/697,169, filed Sep. 6, 2017, which is a continuation of U.S.
Ser. No. 14/774,325, filed Sep. 10, 2015, which is the U.S.
National Stage of International Application No. PCT/US2014/024082,
filed Mar. 12, 2014, which claims the benefit of U.S. Provisional
Application No. 61/788,691 filed Mar. 15, 2013, the entire
disclosures of each of which are incorporated herein by reference
for any and all purposes.
TECHNICAL FIELD
[0002] The invention relates to methods of analyzing heart valves
using 3D images of the heart valves and, more particularly, to
techniques for semi-automating the process of providing heart valve
morphometry for computational stress analysis using 3D
ultrasound.
BACKGROUND
[0003] Mitral valve (MV) disease is common in humans and not
infrequently fatal. Mitral regurgitation, in particular,
demonstrates a strongly-graded relationship between severity and
reduced survival. MV surgery, both repair and replacement, are
commonly exercised treatment options for mitral regurgitation.
Imaging and assessment of the mitral valve has traditionally been
achieved by qualitative 2D ultrasound image analysis. Recently,
real-time three-dimensional transesophageal echocardiography
(rt-3DTEE) has become widely available and implemented. 3D
image-based modeling of the mitral valve is increasingly useful,
and finite element analysis (FEA) has been applied to the MV
frequently over the last 20 years.
[0004] To date, the majority of valve morphometry studies have
employed manual tracing to reconstruct valve geometry from 3D
echocardiographic image data. Therefore, the first objective herein
is to introduce an alternative semi-automated approach to valve
morphometry based on a simple and rapid approach to
user-initialized image segmentation that exploits the contrast
between the mitral valve tissue and surrounding blood pool in
rt-3DTEE images. The valve is subsequently modeled using 3D
continuous medial representation to obtain localized thickness maps
of the mitral leaflets.
[0005] The inventors recently provided a framework for the
application of in vivo MV geometry and FEA to human MV physiology,
pathophysiology, and repair. (see, Xu C, Brinster C J, Jassar A S,
et al. A novel approach to in vivo mitral valve stress analysis. Am
J Physiol Heart Circ Physiol. 299(6):1790-1794, 2010). Therefore, a
second objective herein is to demonstrate that the semi-automated
3D MV model can be loaded with physiologic pressures using FEA,
yielding reasonable and meaningful stress and strain magnitudes and
distributions. Furthermore, the inventors endeavor to demonstrate
this capability in both healthy and diseased human mitral valves.
The methods of the invention address these and other
objectives.
SUMMARY
[0006] Particular embodiments of the methods of the invention use
high-resolution image-derived geometric models of the mitral valve
as anatomically accurate input to finite element analysis (FEA) in
order to estimate localized stress distributions on the mitral
leaflets. A key property of the models is that they volumetrically
represent the leaflets (as structures with locally varying finite
thickness). All previous studies have made very generic assumptions
of leaflet thickness in their FEA studies. For example, leaflet
thickness was assumed to be uniform or have a generic, uniformly
distributed thickness pattern. These assumptions have been based
primarily on ex vivo analysis of porcine valve tissue, rather than
in vivo human valve tissue. A recent review by Rausch et al.
("Mechanics of the Mitral Valve", Biomech Model Mechanobiol. 2012)
indicates that leaflet thickness is one of the most influential
parameters in biomechanical simulations of the mitral valve, which
supports the relevance and significance of the volumetric models as
described herein. The methods of the invention thus assess valve
morphology and thickness in vivo in order to measure or estimate
the stresses on mitral valve leaflets to improve mitral valve
repair durability. As will be apparent to those skilled in the art
from the following description, the techniques described herein
need not be limited to mitral valve leaflets but may also be
applied to other heart valve leaflets.
[0007] Exemplary embodiments of the method of the invention relate
to measuring or estimating stress distributions on heart valve
(e.g., mitral valve) leaflets to, for example, improve heart valve
repair durability by obtaining three-dimensional images of the
heart valve leaflets, segmenting the heart valve leaflets in the
three-dimensional images by capturing and/or quantifying locally
varying thicknesses of the heart valve leaflets in
three-dimensional image data to generate an image-derived
patient-specific model of the heart valve leaflets, and applying
the image-derived patient-specific model of the heart valve
leaflets to a finite element analysis (FEA) algorithm to estimate
stresses on the heart valve leaflets. The patient-specific model of
the heart valve leaflets may be applied to the FEA algorithm using
image-derived thickness measurements of the heart valve leaflets as
input material parameters. The images may be obtained by a number
of techniques including three-dimensional echocardiography.
[0008] Segmenting the heart valve leaflets may be performed
manually, with active contour evolution, with multi-atlas
segmentation, or with a deformable modeling method. For example,
user-initialized three-dimensional active contour evolution based
on region competition may be used to segment the heart valve
leaflets in the three-dimensional image data. User-initialized
regional of interest (ROI) extraction may include construction of a
two-dimensional maximum intensity projection image along an axial
dimension of the image volume of the heart valve leaflet images and
application of adaptive histogram equalization to the projection
image to enhance an annular rim and leaflet coaptation zone of the
heart valve. The user may outline the heart valve and mark a
leaflet coaptation curve in the enhanced projection image, select a
threshold for region competition, and use the resulting information
to initialize three-dimensional active contour segmentation. The
level set method may be used to solve for the final
three-dimensional segmentation.
[0009] The user-initialized segmentation method may be used to
obtain three-dimensional binary images of the anterior and
posterior leaflets of the mitral valve, from which localized
measurements of leaflet thickness can be computed. To obtain
localized leaflet thickness measurements, the shape of each heart
valve leaflet may be modeled with three-dimensional continuous
medial representation (cm-rep). A patient-specific cm-rep of the
heart valve leaflets may be obtained by fitting a deformable medial
model (a cm-rep template of the heart valve leaflets) to binary
segmentations of the heart valve leaflets by Bayesian optimization.
Each heart valve leaflet may be treated as a separate shape, whose
morphological skeleton comprises a single medial manifold. The
medial manifold and surface boundaries of the cm-rep of each heart
valve leaflet may be discretely represented by a triangulated mesh.
Each node of the medial mesh may quantify a localized leaflet
thickness measurement, defined as the chord length or distance
between two boundary points associated with that node on medial
mesh. During Bayesian optimization, the mesh of each heart valve
leaflet may be deformed such that the similarity between the
leaflet mesh and its corresponding segmentation is maximized. For
increased efficiency during model fitting, a Laplace eigenfunction
basis may be defined on the medial mesh such that the medial mesh
is deformed smoothly by modifying coefficients of a small number of
basis functions rather than all vertices of the mesh. Model fitting
may be performed in stages with increasing resolution to enhance
model fitting accuracy and speed. In an exemplary embodiment, an
atrial surface of the fitted cm-rep of each mitral valve leaflet is
applied to the FEA algorithm for finite element analysis, using
locally defined leaflet thickness at each point on the atrial
surface as input information.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] The various novel aspects of the invention will be apparent
from the following detailed description of the invention taken in
conjunction with the accompanying drawings, of which:
[0011] FIG. 1 illustrates application of a semi-automated
segmentation algorithm in an exemplary embodiment.
[0012] FIG. 2 illustrates reconstructions of mid-systolic diseased
(left) and normal (right) mitral valves from 3DE image data,
including (a) the results of image segmentation, (b) the fitted
medial models, and (c) the radial thickness field R mapped to the
medial manifold of each leaflet.
[0013] FIG. 3 illustrates a two-dimensional diagram of medial
geometry.
[0014] FIG. 4 illustrates finite element models of mid-systolic
diseased (A, C) and normal (B, D) mitral valves reconstructed from
rt-3DTEE, in transvalvular (A, B) and oblique (C, D) views.
[0015] FIG. 5 illustrates von Mises stress contour maps of diseased
(left) and normal (right) mitral valves.
DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
[0016] The invention will be described in detail below with
reference to FIGS. 1-5. Those skilled in the art will appreciate
that the description given herein with respect to those figures is
for exemplary purposes only and is not intended in any way to limit
the scope of the invention. All questions regarding the scope of
the invention may be resolved by referring to the appended claims.
For example, though mitral valves are discussed exclusively in the
exemplary embodiment, it will be appreciated that the techniques
described herein may be applied to other heart valves as well.
Methods
Image Acquisition
[0017] Intra-operative rt-3DTEE data sets were obtained from two
patients, one with severe ischemic mitral regurgitation (IMR) and
one without mitral valve disease. The electrocardiographically
gated images were acquired with an iE33 scanner (Philips Medical
Systems, Andover, Mass.) using a 2 to 7 MHz transesophageal
matrix-array transducer over four consecutive cardiac cycles. The
frame rate was 17 to 30 Hz with an imaging depth of 14 to 17 cm.
The image volumes were exported in Cartesian format
(224.times.208.times.208 voxels), with an approximate isotropic
resolution of 0.7 mm. From each rt-3DTEE data series, an image
volume delineating the mitral valve at mid-systole (a single time
point in the cardiac cycle) was selected for analysis.
Semi-Automated Image analysis
[0018] User-Initialized Segmentation
[0019] Segmentation of the mitral leaflets in accordance with the
inventive technique has two steps: user-initialized region of
interest (ROI) extraction, and 3D active contour segmentation based
on region competition. User-initialized ROI extraction begins with
construction of a 2D maximum intensity projection image along the
axial dimension of the image volume. Adaptive histogram
equalization is applied to the projection image to enhance the
annular rim and leaflet coaptation zone of the mitral valve. In
this enhanced projection image, a user outlines the valve and marks
the leaflet coaptation curve in 2D. This information is then used
to initialize 3D segmentation. The user selects a soft threshold
for region competition, and a level set method is used to perform
the final segmentation. The segmentation process is illustrated in
FIG. 1, and the anterior and posterior leaflet segmentations of the
normal and diseased subjects are illustrated in FIG. 2(a).
[0020] FIG. 1 illustrates application of a semi-automated
segmentation algorithm in an exemplary embodiment. In FIG. 1(a),
the user initializes an ROI in a long-axis cross-section of the 3DE
image volume, identifying the valve along the axial dimension. In
FIG. 1(b), the user initializes annular points in a projection
image depicting the valve from an atrial perspective. In FIG. 1(c),
the user shifts posterior annular points into the coaptation zone,
forming an outline of the anterior leaflet. In FIG. 1(d), the user
initialization is used to automatically generate a 3D ROI
containing the valve in the image volume. Finally, in FIG. 1(e), a
final segmentation of the valve is obtained by thresholding and 3D
active contour evolution. (LA=left atrium, LV=left ventricle,
AL=anterior leaflet, PL=posterior leaflet).
[0021] FIG. 2 illustrates reconstructions of mid-systolic diseased
(left) and normal (right) mitral valves from 3DE image data,
including (a) the results of image segmentation, (b) the fitted
medial models, and (c) the radial thickness field R mapped to the
medial manifold of each leaflet. Note that, by definition, the
medial manifold does not extend to the leaflet boundary, as there
is a distance R between the manifold and leaflet surface.
(AL=anterior leaflet, PL=posterior leaflet).
[0022] Mitral Valve Geometric Modeling
[0023] Once 3D binary images of the anterior and posterior leaflets
are obtained, the method of the invention is used to model the
shape of each mitral leaflet using 3D continuous medial
representation (cm-rep). Those skilled in the art will appreciate
that the images may be obtained using other imaging methods besides
echocardiography and that other segmentation methods, such as
multi-atlas segmentation, may also be applied before the cm-rep
model fitting step. Unlike a surface representation that describes
an object's boundary geometry, medial representation is a compact
representation of shape. First introduced by Blum, a shape's medial
axis is defined as the locus of centers of maximal inscribed balls
(MIBs), where each ball is tangent to the object's boundary at a
minimum of two points. In three dimensions, a medial representation
is a locus of tuples (m,R).di-elect cons..sup.3.times..sup.4, where
m is the medial manifold formed by the centers of the MIBs and R
refers to the radii of the MIBs centered at those points or,
equivalently, to the distance between the medial axis and object
surface. For reference, a 2D diagram of medial representation is
presented in FIG. 3. Medial representation is exploited herein for
its ability to assess local variations in leaflet thickness,
derived from the radial thickness field R.
[0024] FIG. 3 illustrates a two-dimensional diagram of medial
geometry. The curve through m represents the medial surface
(skeleton) m. The maximally inscribed circle centered at the dot on
m has radius R, with two spokes (arrows) pointing to two points,
b.sup.+ and b.sup.-, on the object boundaries (curves through
b.sup.+ and b.sup.-). The vector .dbd..sub.mR lies in the tangent
plane of m and points in the direction of greatest change in R.
Thickness is measured herein as chord length, illustrated by the
dotted line spanning the distance between b.sup.+ and b.sup.-.
[0025] In the cm-rep framework, geometric representations of the
mitral leaflets are obtained by fitting a deformable medial model,
also referred to as a template, to its binary segmentation by
Bayesian optimization. Each leaflet is treated as a separate shape,
so the valve is modeled as two separate cm-reps, where each leaflet
is a simple object whose skeleton consists of a single medial
manifold. The medial manifold of each leaflet is represented by a
mesh with 500 to 600 nodes and 900 to 1000 triangulated elements
and is associated with a triangulated boundary mesh that represents
the surface of the leaflet. Template fitting, or mesh deformation,
consists of three stages: one alignment stage, one multi-resolution
fitting stage, and a final deformation stage where both leaflets
are simultaneously fitted to the leaflet segmentations. (1) During
the first stage, Jenkinson's FLIRT affine registration tool is used
to obtain a similarity transform that aligns the leaflet templates
with their corresponding segmentations. (2) The leaflet medial
models are then independently deformed to fit the binary leaflet
segmentations at three different resolutions. The objective
function minimized during deformation incorporates the volumetric
overlap error between the medial model and binary segmentation, as
well as regularization terms and inequality constraints required by
inverse skeletonization. (3) Finally, to correct for any
intersection of the leaflet models, the medial models of the two
leaflets are combined into a single model during the third stage of
fitting. During the simultaneous fitting of both leaflets, a
leaflet intersection penalty term is used to correct and prevent
intersection of the leaflets' medial models. For increased
efficiency during the last two steps of template fitting, the
Laplace eigenfunction basis is defined on the medial template such
that it can be deformed smoothly by modifying the coefficients of a
small number of basis functions rather than all vertices of the
template mesh. The results of model fitting are shown in FIG. 2(b),
and the radial thickness field R mapped to the medial manifold of
each leaflet is shown in FIG. 2(c) for both the normal and diseased
valves.
[0026] In the methods described herein, the atrial surface of the
fitted boundary mesh of each leaflet is used for finite element
analysis. At each node of the mesh, localized leaflet thickness is
quantified as chord length, i.e. the distance between the two
boundary patches b.sup.+ and b.sup.- associated with each point m
on the medial manifold, as shown in FIG. 3.
Finite Element Analysis
[0027] To obtain high-quality meshes for complex shapes such as
mitral leaflets, the atrial sides of the leaflet surfaces acquired
from semi-automated image analysis were first imported into
HyperMesh 10.0 (Altair Inc.) as triangular elements, where raw
nodal points were added, suppressed, or replaced to refine the
leaflets' topological details without changing geometrical shape.
The mesh quality criteria included element Jacobian, element size,
minimum and maximum angles, and skewness. The refined triangulated
leaflet surfaces were modeled as thin shells (type S3R). The
thickness measurements acquired from the rt-3DTEE data were
interpolated and assigned to each node in the refined leaflet mesh
using Matlab (the Mathworks, Natick, Mass.). Leaflet tissue was
assumed to be orthotropic and linearly elastic, with a Poisson's
ratio of 0.49, and Young's modulus determined from excised porcine
tissue data (Table 1). The coaptation area between the anterior and
posterior leaflet was defined as an interface pair with coefficient
of friction .mu.=0.3. Thirty-two chordae originating from each
papillary muscle tip were inserted symmetrically into the anterior
and posterior leaflets along the free edges of the leaflets
(primary chordae), or more peripherally (secondary chordae).
Papillary muscle tips were modeled as single points hinged in space
associated with rotational freedom only. Chordae tendinae were
represented by strings connecting the papillary muscle tips to the
insertion points on the leaflets, and modeled by a tension-only
truss element (type T3D2). Commercial FEA software (ABAQUS/Explicit
6.9, HKS Inc. Pawtucket, R.I.) was used to analyze the deformation
and resulting stress distribution in the mitral valve models, as
previously described by the inventors in the afore-mentioned
article. Systolic loading was accomplished via application of an 80
mmHg pressure gradient across the mitral valve. Stress, strain and
displacement were recorded as output variables.
TABLE-US-00001 TABLE 1 Mitral valve material properties used in FEA
model Anterior Posterior Primary Secondary Parameter leaflet
leaflet chordae chordae Cross-sectional -- -- 0.4 0.7 Area
(mm.sup.2) E.sub.circumferential (Pa) 6.20 .times. 10.sup.6 2.35
.times. 10.sup.6 4.20 .times. 10.sup.7 2.20 .times. 10.sup.7
E.sub.radial (Pa) 2.10 .times. 10.sup.6 1.887 .times. 10.sup.6 --
-- Poisson's Ratio 0.49 0.49 0.49 0.49 Density (kg/m.sup.3) 1.04
.times. 10.sup.3 1.04 .times. 10.sup.3 1.04 .times. 10.sup.3 1.04
.times. 10.sup.3
RESULTS
[0028] The mean and maximal AL and PL thicknesses derived from 3DE
are reported in Table 2, for both the normal and the diseased
mitral valve. The regurgitant orifice of the diseased valve was
clearly imaged and depicted in the 3D model, as demonstrated in
FIG. 4. FIG. 4 illustrates finite element models of mid-systolic
diseased (A, C) and normal (B, D) mitral valves reconstructed from
rt-3DTEE, in transvalvular (A, B) and oblique (C, D) views. (Nodes
and elements have been reduced for visualization purposes.)
[0029] FIG. 5 illustrates von Mises stress contour maps of diseased
(left) and normal (right) mitral valves predicted by FEA. Note the
persistent orifice between the anterior and posterior leaflets in
the regurgitant valve despite some conformational change
(deformation) with pressure loading. The regurgitant orifice of the
diseased valve is evident in the loaded valve, though some
conformational change (deformation) has occurred. The peak and mean
stresses in the bellies of the AL and PL's of each valve were
similar (Table 3). Note that for both mitral valves, the peak AL
belly stress was larger than the peak PL belly stress, and the mean
AL stress was significantly larger than the mean PL stress
(P<0.001 in both the normal and the diseased valve).
TABLE-US-00002 TABLE 2 Mitral valve leaflet thicknesses Mean
Maximum Nodes (mm) (mm) in mesh Anterior leaflet (normal) 2.6 4.7
13735 Posterior leaflet (normal) 2.3 4.8 9148 Anterior leaflet
(diseased) 2.4 5.1 9235 Posterior leaflet (diseased) 2.4 4.2
4125
TABLE-US-00003 TABLE 3 von Mises stress Mean (kPa) P-value Peak
(kPa) Anterior leaflet belly (normal) 97.1 .+-. 37.2 215.5
Posterior leaflet belly (normal) 37.6 .+-. 14.4 <.001 85.5
Anterior leaflet belly (diseased) 81.7 .+-. 19.2 192.8 Posterior
leaflet belly (diseased) 39.2 .+-. 16.3 <.001 134.8
DISCUSSION
[0030] A semi-automated and integrated methodology for imaging,
segmenting, modeling, and deriving computationally-predicted
pressure-derived mitral valve leaflet stresses is presented herein,
and points the way towards intraoperative and periprocedural
guidance from morphometric and stress modeling of the mitral
valve.
[0031] The methods described herein provide an approach to valve
morphometry that provides a comprehensive, automated assessment of
3D valve geometry--in both normal and diseased mitral valves--by
ultrasound image analysis. This is accomplished using an efficient
segmentation strategy that exploits the contrast in 3D
transesophageal images and uses projections of 3D data to eliminate
the need for the user to navigate a 3D image volume during
initialization. Though not emphasized herein, the incorporation of
deformable registration with cm-rep allows for a compact parametric
representation of valve shape, from which a number of clinically
significant features can be automatically derived. In addition,
with the ability to establish points of correspondence on valves of
different subjects and on the same valve at different time points,
deformable modeling with cm-rep lays the foundation for statistical
studies of time-dependent valve morphology.
[0032] A further advantage of the image analysis and segmentation
algorithms described is that an objective measure of local mitral
leaflet thickness is provided. While these measurements have not
been validated in vivo, the inventors are in the process of doing
so. Ex vivo human mitral valve leaflet thicknesses have been
described, and the image-derived thicknesses are generally
consistent with those pathologic measurements and with prior
echocardiographic measures of leaflet thicknesses in normal human
mitral valves. The present invention presents the first FEA
simulation of the mitral valve incorporating high resolution in
vivo measurements of leaflet thickness.
[0033] The ability to reliably estimate patient-specific mitral
leaflet and chordal stresses in vivo has important clinical
implications. Repair failure as manifest by the development of
recurrent mitral regurgitation has recently been demonstrated to be
far more common than originally believed. Recent studies have also
shown that repair failures often result from stress related
phenomenon such as chordal rupture, leaflet suture line disruptions
and annuloplasty ring dehiscence. The ability to assess leaflet and
chordal stresses in repaired valves will, with clinical experience,
likely lead to improved surgical results by identifying patients
with high stress valves in the early post-operative period. Such
patients could either have re-repair or valve replacement before
ever leaving the operating room, or could be subjected to closer
post-operative clinical follow-up.
[0034] There are some evident limitations of the methods described
herein. First, the stress maps derived have not been tested against
in vivo experimental results. However, this deficit is
characteristic of all prior work on the stress and strain behavior
of the mitral valve apparatus: it is difficult or impossible to
measure in vivo strains of heart valves except in discrete matrices
of transducers or markers. Second, the FEA utilizes a
less-than-comprehensive mitral valve model: whereas the leaflet
surface profile and papillary muscle tips are accurately determined
by rt-3DTEE, the chordae tendinae were not reliably imaged and so
their incorporation in the model is, at best, heuristically
motivated. This solution is admittedly suboptimal, but similar to
that adopted in prior noninvasive mitral valve FEA studies (Votta,
et al. 2008, "Mitral valve finite-element modeling from ultrasound
data: a pilot study for a new approach to understand mitral
function and clinical scenarios," Philosophical transactions Series
A, Mathematical, physical, and engineering sciences, 366, pp.
3411-3434). Prot et. al. used ex vivo examination of porcine valves
to determine the number and insertions of secondary chordae (Prot
et al., "Finite element analysis of the mitral apparatus: annulus
shape effect and chordal force distribution," Biomechanics and
Modeling in Mechanobiology, Vol. 8, pp. 43-55), but this approach
is clearly impossible in most human studies. In addition, the
material properties model (linearly elastic) used is relatively
simplified; however, the closed mitral valve has been shown to have
a linear stress-strain relationship over the physiologic range of
pressures. Finally, homogeneous and uniform material properties
were used in implementing the methods described herein. It is
reasonable to presume that leaflet material properties will be
different in diseased and healthy mitral valves, and may vary
regionally in a single valve. Nevertheless, the current research
emphasizes the dependence of mechanical stress on geometric
derangements in the diseased mitral valve.
[0035] Recently, the inventors have demonstrated that FEA modeling
of the in vivo human mitral valve using high-resolution 3D imaging
is reasonable and useful for stress prediction in mitral valve
pathologies and repairs (Xu). The methods described herein extend
and amplify those results, and promises near-real-time stress
analysis in the human mitral valve using automated 3DE image
analysis and modeling, and FEA. Therefore, a rational approach to
in vivo mitral valve stress analysis incorporates realistic empiric
material properties of leaflets and chordae, 3D imaging,
semi-automated valve segmentation and modeling, and FEA.
[0036] Those skilled in the art will also appreciate that the
invention may be applied to other applications and may be modified
without departing from the scope of the invention. For example, the
techniques described herein are not limited to measurement of
mitral valve morphometry but also may be applied to other heart
valves as well. Also, the methods of the invention may be applied
to images obtained using other imaging methods besides
echocardiography that permit quantification of the thickness of the
heart valves. In addition, other segmentation methods, such as
manual tracing or multi-atlas segmentation, may also be applied
before the cm-rep model fitting. Another possibility is to fit the
cm-rep model directly to the three-dimensional grayscale image.
Accordingly, the scope of the invention is not intended to be
limited to the exemplary embodiments described above, but only by
the appended claims.
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